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Mixture models and applications

Author: Nizar Bouguila; Wentao Fan
Publisher: Cham, Switzerland : Springer, [2020]
Series: Unsupervised and semi-supervised learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bouguila, Nizar.
Mixture Models and Applications.
Cham : Springer, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nizar Bouguila; Wentao Fan
ISBN: 9783030238766 3030238768
OCLC Number: 1112422502
Notes: 4.5.1.1 Experimental Framework and Results for VisTex Texture Dataset
Description: 1 online resource (356 pages)
Contents: Intro; Preface; References; Contents; Contributors; Part I Gaussian-Based Models; 1 A Gaussian Mixture Model Approach to ClassifyingResponse Types; 1.1 Background; 1.1.1 The Influence of Prior Information During Interrupted Visual Search; 1.1.2 Quantifying Individual Differences During the Interrupted Search Task; 1.1.3 An Alternative Approach to Classifying Response Types During Interrupted Search; 1.1.4 Aims of This Chapter; 1.2 Methods; 1.2.1 Data Collection; 1.2.2 Overview of Approach; 1.2.3 Gaussian Mixture Models; 1.2.4 Expectation-Maximisation Algorithm 1.2.5 Estimation of Mixture Model Parameters1.2.5.1 Initialisation; 1.2.5.2 Expectation Step; 1.2.5.3 Maximisation Step; 1.2.5.4 Convergence Criteria; 1.2.6 Log Probability Ratio; 1.3 Results; 1.3.1 Parameter Estimation of Response Distributions; 1.3.2 Evaluation of Previous Classification Criteria; 1.3.3 Comparison of Classification Methods; 1.4 Discussion; Appendix: Additional Methods; Participants; Stimuli Presentation; Procedure; References; 2 Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures; 2.1 Introduction; 2.2 Background; 2.3 Trajectory Generation 2.3.1 Dynamical System2.3.2 Optimization Objective; 2.3.3 Tracking Formulation; 2.3.4 Stochastic Solution; 2.3.5 Periodic Motions; 2.4 User Interface; 2.4.1 Semi-tied Structure; 2.5 Conclusions; References; 3 Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series; 3.1 Introduction; 3.2 Movement Primitives; 3.2.1 Radial Basis Functions (RBFs); 3.2.1.1 Gaussian Mixture Regression (GMR); 3.2.2 Bernstein Basis Functions; 3.2.3 Fourier Basis Functions; 3.2.4 Ergodic Control 4.2.3 Mixture of Bounded Asymmetric Gaussian Distribution for Multidimensional Data4.2.4 Parameters Learning; 4.2.4.1 Mixing Parameter Estimation; 4.2.4.2 Mean Parameter Estimation; 4.2.4.3 Left Standard Deviation Estimation; 4.2.4.4 Right Standard Deviation Estimation; 4.3 Textual Spam Detection; 4.4 Object Categorization via Bounded Asymmetric Gaussian Mixture Model; 4.4.1 Experiments and Results; 4.4.1.1 Experimental Framework and Results: Caltech 101 Dataset; 4.4.1.2 Experimental Framework and Results: Corel Dataset; 4.5 Texture Image Clustering; 4.5.1 Experiments and Results
Series Title: Unsupervised and semi-supervised learning.
Responsibility: Nizar Bouguila, Wentao Fan, editors.

Abstract:

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.

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